import numpy


class DataSet(object):
    def __init__(self, images, labels):
        assert images.shape[0] == labels.shape[0], (
          "images.shape: %s labels.shape: %s" % (images.shape,
                                                 labels.shape))
        self._num_examples = images.shape[0]

        # Convert shape from [num examples, rows, columns, depth]
        # to [num examples, rows*columns] (assuming depth == 1)

        #assert images.shape[3] == 1
        #images = images.reshape(images.shape[0],
        #                      images.shape[1] * images.shape[2])

        # Convert from [0, 255] -> [0.0, 1.0].
        images = images.astype(numpy.float32)

        images = numpy.multiply(images, 1.0 / 255.0)

        #images = numpy.multiply(images, 1.0 / 7.0 )

        self._images = images
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0

    @property
    def images(self):
        return self._images

    @property
    def labels(self):
        return self._labels

    @property
    def num_examples(self):
        return self._num_examples

    @property
    def epochs_completed(self):
        return self._epochs_completed

    def set_index(self,v):
        self._index_in_epoch = v;

    def next_batch(self, batch_size):
        """Return the next `batch_size` examples from this data set."""
        start = self._index_in_epoch
        self._index_in_epoch += batch_size
        if self._index_in_epoch > self._num_examples:
            # Finished epoch
            self._epochs_completed += 1
            # Shuffle the data
            perm = numpy.arange(self._num_examples)
            numpy.random.shuffle(perm)
            self._images = self._images[perm]
            self._labels = self._labels[perm]
            # Start next epoch
            start = 0
            self._index_in_epoch = batch_size
            assert batch_size <= self._num_examples
        end = self._index_in_epoch
        return self._images[start:end], self._labels[start:end]